PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing

2025-08-14

AI Summary

Existing paper retrieval systems rely on abstract-based indexing, limiting their ability to support precise queries over fine-grained technical details—such as module configurations. To address this, we propose PaperRegister, the first framework introducing a hierarchical registration indexing mechanism. It constructs a multi-granularity index tree spanning topics, methods, components, and parameters via hierarchical text parsing, modular information extraction, and adaptive matching. The framework synergistically integrates offline index construction with online dynamic retrieval, overcoming the granularity bottleneck inherent in conventional abstract-only indexing. Experiments demonstrate that PaperRegister achieves state-of-the-art performance across both coarse- and fine-grained retrieval tasks, significantly outperforming baseline methods—especially for queries targeting technical specifications. The system exhibits strong practicality and scalability, enabling efficient, precise navigation of technical knowledge in scientific literature.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Computer Vision: Image and Video Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web crawling and indexing
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Paper search is an important activity for researchers, typically involving using a query with description of a topic to find relevant papers. As research deepens, paper search requirements may become more flexible, sometimes involving specific details such as module configuration rather than being limited to coarse-grained topics. However, previous paper search systems are unable to meet these flexible-grained requirements, as these systems mainly collect paper abstracts to construct index of corpus, which lack detailed information to support retrieval by finer-grained queries. In this work, we propose PaperRegister, consisted of offline hierarchical indexing and online adaptive retrieval, transforming traditional abstract-based index into hierarchical index tree for paper search, thereby supporting queries at flexible granularity. Experiments on paper search tasks across a range of granularity demonstrate that PaperRegister achieves the state-of-the-art performance, and particularly excels in fine-grained scenarios, highlighting the good potential as an effective solution for flexible-grained paper search in real-world applications. Code for this work is in https://github.com/Li-Z-Q/PaperRegister.

Problem

• Enables flexible-grained paper search via hierarchical indexing
• Addresses limitations of abstract-based search systems
• Supports queries with specific details and varying granularity

Innovation

• Hierarchical index tree for flexible search
• Offline indexing and online adaptive retrieval
• Supports queries at flexible granularity